Calculate value of management flexibility

Price only executable management flexibility on one coherent scenario set: compare a frozen static plan, a nonanticipative adaptive policy and a perfect-information upper bound; separate expected flexibility from remaining information value, tail underperformance and tail regret; quantify liquidity-risk reduction; and refuse value when policy integrity, evidence or dominance fails.

Constrained optimization Calculate CTOs Tech CEOs IT managers Team leads Investors

What it's for

Gives boards and investors a defensible answer to 'what is the option value of waiting and adapting?' while visibly separating real flexibility from perfect hindsight.

What you give it

Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

Field Type Role Required
dominance_tolerance number ≥ 0, ≤ 0.01 Your calibration Optional
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_adaptive_liquidity_breach_probability number ≥ 0, ≤ 1 Your calibration Optional
maximum_tail_adaptive_regret number ≥ 0 Your calibration Optional
maximum_unverified_probability_mass number ≥ 0, ≤ 1 Your calibration Optional
minimum_expected_flexibility_value number Your calibration Optional
minimum_liquidity_buffer number Your calibration Optional
policy_integrity_supported boolean Your calibration Yes
scenarios array of objects (8 fields) ≥ 2 items Evidence Yes
tail_probability number > 0, ≤ 0.5 Your calibration Optional

Each scenarios record

Field Type Required
adaptive_minimum_liquidity number Yes
adaptive_net_value number Yes
evidence_verified boolean Yes
id string (non-empty) Yes
perfect_information_net_value number Yes
probability number (> 0, ≤ 1) Yes
static_minimum_liquidity number Yes
static_net_value number Yes
Example input 
{
  "maximum_adaptive_liquidity_breach_probability": 0,
  "minimum_liquidity_buffer": 20,
  "policy_integrity_supported": true,
  "scenarios": [
    {
      "adaptive_minimum_liquidity": 20,
      "adaptive_net_value": 55,
      "evidence_verified": true,
      "id": "adverse",
      "perfect_information_net_value": 70,
      "probability": 0.4,
      "static_minimum_liquidity": -10,
      "static_net_value": 30
    },
    {
      "adaptive_minimum_liquidity": 20,
      "adaptive_net_value": 120,
      "evidence_verified": true,
      "id": "favorable",
      "perfect_information_net_value": 130,
      "probability": 0.6,
      "static_minimum_liquidity": 20,
      "static_net_value": 100
    }
  ],
  "tail_probability": 0.4
}

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

Example output 
{
  "configuration": {
    "dominance_tolerance": 0,
    "maximum_adaptive_liquidity_breach_probability": 0,
    "maximum_tail_adaptive_regret": 1000000000000000000,
    "maximum_unverified_probability_mass": 0,
    "minimum_expected_flexibility_value": 0,
    "minimum_liquidity_buffer": 20,
    "policy_integrity_supported": true,
    "tail_probability": 0.4
  },
  "decision": "management_flexibility_value_supported",
  "failed_gates": [],
  "guardrails": [
    "Static, adaptive and perfect-information values must be evaluated on the same coherent scenarios, prices, horizon, discounting and full action costs. Independently sorted distributions cannot price flexibility.",
    "The adaptive policy must first pass a nonanticipativity and information-timing audit. Any value obtained from acting on a signal before it is observable is clairvoyance and is excluded from supported management flexibility.",
    "Perfect information is an unattainable upper bound, not a forecast. Scenario value does not prove realized or causal value; validate deployed policies prospectively and reconcile outcomes to finance records."
  ],
  "method": "common_scenario_flexibility_evsi_evpi_and_tail_regret",
  "scenario_diagnostics": [
    {
      "adaptive_liquidity_breach": false,
      "adaptive_net_value": 55,
      "adaptive_regret": 15,
      "adaptive_underperformance": 0,
      "evidence_verified": true,
      "flexibility_value": 25,
      "perfect_information_net_value": 70,
      "probability": 0.4,
      "scenario_id": "adverse",
      "static_liquidity_breach": true,
      "static_net_value": 30
    },
    {
      "adaptive_liquidity_breach": false,
      "adaptive_net_value": 120,
      "adaptive_regret": 10,
      "adaptive_underperformance": 0,
      "evidence_verified": true,
      "flexibility_value": 20,
      "perfect_information_net_value": 130,
      "probability": 0.6,
      "scenario_id": "favorable",
      "static_liquidity_breach": false,

Truncated for display — the full payload is 66 lines.

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

  1. 1 Freeze scenario probabilities and full-cost static, adaptive and perfect-information net values plus minimum liquidity on the same horizon, currency, price and discount basis.
  2. 2 Require an upstream scenario-tree integrity clearance, verify the perfect-information bound scenario by scenario, and compute expected flexibility, EVPI, remaining information value and the fraction of the attainable information advantage captured by executable adaptation.
  3. 3 Measure adaptive underperformance probability, tail-CVaR underperformance and regret, static/adaptive liquidity breaches and unverified probability mass; support value only when every governed economic, risk and evidence gate clears.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Assumptions & guardrails

  • Objectives use commensurable locally governed value units, constraints reflect real feasibility, and uncertainty covers plausible adverse inputs.
  • The three alternatives use identical terminal scenarios and include all implementation, financing, delay, transaction and reversal costs; perfect information is optimized within the same feasible action set and cannot be dominated by a feasible static or adaptive plan.
  • The recommendation is optimal only for its stated objective, feasible set, evidence, and solver guarantee; it is not a universal management optimum.
  • This is scenario-model value, not realized or causal value. Never market perfect-information value as achievable, and never count value from scenario-specific actions taken before their distinguishing signal.

Minimum evidence

  • scenarios: at least 2 rows/items
  • policy_integrity_supported: required and organization-defined

How to validate it

Backtest the chosen action against simple feasible baselines on held-out scenarios, sweep costs/constraints/risk tolerance, and require constraint feasibility under adverse inputs.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

  • finance-approved common-scenario comparison of the frozen static plan, audited nonanticipative policy and same-feasible-set perfect-information upper bound with complete cash, cost, discounting and liquidity semantics
  • scenario perimeter/probabilities, incremental value and counterfactual, currency/horizon/discounting, full action/financing/reversal cost, liquidity definition, integrity lineage, evidence verification, breach/tail/value appetites and finance reconciliation

Calibration workflow

  1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
  2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
  3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
  4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
  5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
  6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 20 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own. 

gitrevio_capabilities_search
  { "q": "price only executable management flexibility on" }
  → finds "calculate_value_of_management_flexibility"

gitrevio_capability_describe
  { "capability_id": "calculate_value_of_management_flexibility" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "calculate_value_of_management_flexibility", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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